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@paunikar-jayesh started using tool PHP , 2 hours, 3 minutes ago.

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@paunikar-jayesh started using tool MySQL , 2 hours, 3 minutes ago.

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@paunikar-jayesh started using tool Laravel , 2 hours, 3 minutes ago.

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@laura_garcia shared a post, 20 hours ago

Software Developer, RELIANOID

DevOpsCon Amsterdam 2026

- 𝗗𝗲𝘃𝗢𝗽𝘀𝗖𝗼𝗻 𝗔𝗺𝘀𝘁𝗲𝗿𝗱𝗮𝗺 𝟮𝟬𝟮𝟲 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝗿𝗻𝗲𝗿! - Amsterdam, Netherlands April 20–24, 2026 Simplify complexity, amplify agility, and accelerate innovation. Join DevOpsCon Amsterdam 2026 — one of the leading conferences for professionals working with CI/CD, Kubernetes, Platform Engineering, ..

Story WrapPixel Team

@sanjayjoshi shared a post, 1 day, 22 hours ago

10+ Shadcn Table Components, Blocks & Tools

A curated list of Shadcn table components and blocks you can use in React and Next.js projects to build clean, flexible, and production-ready data tables faster.

Story Keploy Team

@sancharini shared a post, 2 days ago

Black Box Testing Techniques to Improve Test Coverage

Learn black box testing techniques to improve test coverage. Explore methods like equivalence partitioning, boundary value analysis, and more with practical examples.

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@laura_garcia shared a post, 2 days, 1 hour ago

Software Developer, RELIANOID

𝗪𝗼𝗿𝗹𝗱 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗗𝗮𝘆

🚀 𝗪𝗼𝗿𝗹𝗱 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗗𝗮𝘆 𝗶𝘀 𝗵𝗲𝗿𝗲… and it’s not just science fiction anymore. Quantum computing is rapidly moving from theory to reality—and with it comes a 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 that organizations simply can’t ignore. Here’s the uncomfortable truth: 👉 The same technology that promises breakthrou..

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@hamzmu shared a link, 2 days, 17 hours ago

Fellow, Rootly

Using Graphify to turn Incident Data into a Knowledge Graph

Karpathy said we should build LLM knowledge bases. 48 hours later made Graphify was made: one command, full semantic knowledge graph.

We applied the idea to incident data turning them into a queryable and interactable semantic graph. This lets us see past fixes, predict failures, cluster services, cut alert noise, and reveal team load in seconds.

If you’re using Rootly, here is a small plugin to explore your incident data.

Check it out: github.com/Rootly-AI-Labs/rootly-graphify-importer

Interactive knowledge graph visualization of incident management data showing clustered services, alerts, and responders with connected nodes and relationships in Graphify

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@laura_garcia shared a post, 2 days, 18 hours ago

Software Developer, RELIANOID

Strengthen Your MFA with Google Authenticator and RELIANOID

🔐 Strengthen Your MFA with Google Authenticator and RELIANOID At RELIANOID, we take authentication seriously. We've just published a new technical guide on how to integrate Google Authenticator into the RELIANOID MFA Portal, using Active Directory or LDAP to manage user secrets. ✅ Understand TOTP vs..

2FA with AD_LDAP and Google Authenticator

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@roock started using tool Terraform , 5 days, 11 hours ago.

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.